ISYE 6501 Exam 2 Questions And Answers Verified 100% Correct

ISYE 6501 Exam 2 Questions And Answers Verified 100% Correct Which of these is generally a good reason to remove an outlier from your data set? A. The outlier is an incorrectly-entered data, not real data. B. Outliers like this only happen occasionally - ANSWER A. If the data point isn't a true one, you should remove it from your data set. What is an outlier? - ANSWER A data point that is very different from the rest What graph or plot can we use to find outliers? - ANSWER box and whisker plot What are the parts of a box and whisker plot? - ANSWER The bottom and top of the box are the 25th and 75th percentile. The middle value is the median. The whiskers stretch up and down to reasonable range of values (10 and 90th or 5th and 95th percentiles) Where would outliers exist in a box and whisker plot? - ANSWER Outside of the whiskers What are some ways to deal with outliers that are bad data? - ANSWER Omit them or use imputation What can change detection be used for? - ANSWER Determining whether action might be needed, determining impact of past action, determining changes to help plan What is Cumulative Sum (CUSUM) used for? - ANSWER detect increase, decrease, or both What is C used for in the Cusum formula? - ANSWER Since we expect there to be some randomness, we include a value C to pull the running total down If we have a larger C ... - ANSWER the harder for S_t to get large and the less sensitive the method will be If we have a smaller C ... - ANSWER the more sensitive the method is because S-t can get larger faster What factors go into finding the right values of C and T? - ANSWER how costly it is if the model takes a long time to notice a change, and how costly it is if the model thinks it has found a change that really isn't there Why are hypothesis tests often not sufficient for change detection? - ANSWER They often are slow to detect changes. Hypothesis tests generally have high threshold levels, which makes them slow to detect changes. In the CUSUM model, having a higher threshold T makes it ... - ANSWER detect changes slower, and less likely to falsey detect changes. In the exponential smoothing equation S_t = alpha times x_t + (1-alpha) times S_{t- 1} a value closer to 1 is chosen if... - ANSWER There's less randomness, so we're more willing to trust the observation. We put more weight on the observation x_t than the previous estimate S_{t-1} What are some of the consequences of outliers in k-means? - ANSWER It will drag the cluster center artificially to one side Because k-means is a heuristic and thus fast what can we do? - ANSWER run it several times choosing different clusters centers and choose the best one and we can choose different values of k How does bias/variance change as k changes in KNN? - ANSWER the higher the k, the higher the bias; the lower the k, the higher the variance. when k = 1, that is the most complex model and thus likely to overfit the data How do we find the best value of k in k means? - ANSWER Elbow method: we calculate the total distance of each data point to its cluster center and plot it in two dimensions. We look for the kik in the graph When clustering for prediction, how do we choose the prediction? - ANSWER When we see a new point, we just choose whichever cluster center is closest